Name
Abstract | ||
|---|---|---|
Intelligent agents operate in an environment that can be mostly not dynamic. This requires learning by trial and error in order to know better the environment and reach their goals. They also need cooperation among themselves. We use reinforcement learning and game theory techniques to discuss how intelligent agents learn and cooperate. As known, one of the properties of agents is that they are social. They must therefore be cooperative in the social environment where they are. This is possible by learning the environment facts by doing, sharing instantaneous information and learned knowledge. The cooperative agents will perform better than independent agents. What is the advantage of such cooperation? As an example, in this paper we shall especially show how cooperative trading agents can maximize their profits due to a good coordination by playing Nash equilibrium to ensure that each agent chooses the best strategy which gives a good payoff. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/APSCC.2008.179 | APSCC |
Keywords | Field | DocType |
agents coordination,intelligent agent,reinforcement learning,cooperative agent,multiagent,learning (artificial intelligence),nash equilibrium,multi-agent systems,multiagent system,environment fact,cooperative trading agent,game theory,good coordination,nash q-learning,intelligent agent coordination,intelligent agent learning,good payoff,better strategy,best strategy,nash equilibrium strategy,social environment,game theory technique,data mining,games,learning artificial intelligence,probability density function,profitability,multi agent systems | Trial and error,Intelligent agent,Computer science,Risk analysis (engineering),Multi-agent system,Game theory,Artificial intelligence,Nash equilibrium,Distributed computing,Reinforcement learning,Profit (economics),Stochastic game | Conference |
ISBN | Citations | PageRank |
978-0-7695-3473-2 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lasheng Yu | 1 | 10 | 2.81 |
| Emmanuel Masabo | 2 | 6 | 1.64 |
| Chantal Mutimukwe | 3 | 0 | 0.34 |